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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.ensemble</span></code>.BaggingRegressor</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-ensemble-baggingregressor">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.ensemble.BaggingRegressor</span></code></a></li>
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  <div class="section" id="sklearn-ensemble-baggingregressor">
<h1><a class="reference internal" href="../classes.html#module-sklearn.ensemble" title="sklearn.ensemble"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.ensemble</span></code></a>.BaggingRegressor<a class="headerlink" href="#sklearn-ensemble-baggingregressor" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="sklearn.ensemble.BaggingRegressor">
<em class="property">class </em><code class="sig-prename descclassname">sklearn.ensemble.</code><code class="sig-name descname">BaggingRegressor</code><span class="sig-paren">(</span><em class="sig-param">base_estimator=None</em>, <em class="sig-param">n_estimators=10</em>, <em class="sig-param">max_samples=1.0</em>, <em class="sig-param">max_features=1.0</em>, <em class="sig-param">bootstrap=True</em>, <em class="sig-param">bootstrap_features=False</em>, <em class="sig-param">oob_score=False</em>, <em class="sig-param">warm_start=False</em>, <em class="sig-param">n_jobs=None</em>, <em class="sig-param">random_state=None</em>, <em class="sig-param">verbose=0</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/ensemble/_bagging.py#L835"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.ensemble.BaggingRegressor" title="Permalink to this definition">¶</a></dt>
<dd><p>A Bagging regressor.</p>
<p>A Bagging regressor is an ensemble meta-estimator that fits base
regressors each on random subsets of the original dataset and then
aggregate their individual predictions (either by voting or by averaging)
to form a final prediction. Such a meta-estimator can typically be used as
a way to reduce the variance of a black-box estimator (e.g., a decision
tree), by introducing randomization into its construction procedure and
then making an ensemble out of it.</p>
<p>This algorithm encompasses several works from the literature. When random
subsets of the dataset are drawn as random subsets of the samples, then
this algorithm is known as Pasting <a class="reference internal" href="#r4d113ba76fc0-1" id="id1"><span>[R4d113ba76fc0-1]</span></a>. If samples are drawn with
replacement, then the method is known as Bagging <a class="reference internal" href="#r4d113ba76fc0-2" id="id2"><span>[R4d113ba76fc0-2]</span></a>. When random subsets
of the dataset are drawn as random subsets of the features, then the method
is known as Random Subspaces <a class="reference internal" href="#r4d113ba76fc0-3" id="id3"><span>[R4d113ba76fc0-3]</span></a>. Finally, when base estimators are built
on subsets of both samples and features, then the method is known as
Random Patches <a class="reference internal" href="#r4d113ba76fc0-4" id="id4"><span>[R4d113ba76fc0-4]</span></a>.</p>
<p>Read more in the <a class="reference internal" href="../ensemble.html#bagging"><span class="std std-ref">User Guide</span></a>.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.15.</span></p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>base_estimator</strong><span class="classifier">object or None, optional (default=None)</span></dt><dd><p>The base estimator to fit on random subsets of the dataset.
If None, then the base estimator is a decision tree.</p>
</dd>
<dt><strong>n_estimators</strong><span class="classifier">int, optional (default=10)</span></dt><dd><p>The number of base estimators in the ensemble.</p>
</dd>
<dt><strong>max_samples</strong><span class="classifier">int or float, optional (default=1.0)</span></dt><dd><p>The number of samples to draw from X to train each base estimator.</p>
<ul class="simple">
<li><p>If int, then draw <code class="docutils literal notranslate"><span class="pre">max_samples</span></code> samples.</p></li>
<li><p>If float, then draw <code class="docutils literal notranslate"><span class="pre">max_samples</span> <span class="pre">*</span> <span class="pre">X.shape[0]</span></code> samples.</p></li>
</ul>
</dd>
<dt><strong>max_features</strong><span class="classifier">int or float, optional (default=1.0)</span></dt><dd><p>The number of features to draw from X to train each base estimator.</p>
<ul class="simple">
<li><p>If int, then draw <code class="docutils literal notranslate"><span class="pre">max_features</span></code> features.</p></li>
<li><p>If float, then draw <code class="docutils literal notranslate"><span class="pre">max_features</span> <span class="pre">*</span> <span class="pre">X.shape[1]</span></code> features.</p></li>
</ul>
</dd>
<dt><strong>bootstrap</strong><span class="classifier">boolean, optional (default=True)</span></dt><dd><p>Whether samples are drawn with replacement. If False, sampling
without replacement is performed.</p>
</dd>
<dt><strong>bootstrap_features</strong><span class="classifier">boolean, optional (default=False)</span></dt><dd><p>Whether features are drawn with replacement.</p>
</dd>
<dt><strong>oob_score</strong><span class="classifier">bool</span></dt><dd><p>Whether to use out-of-bag samples to estimate
the generalization error.</p>
</dd>
<dt><strong>warm_start</strong><span class="classifier">bool, optional (default=False)</span></dt><dd><p>When set to True, reuse the solution of the previous call to fit
and add more estimators to the ensemble, otherwise, just fit
a whole new ensemble. See <a class="reference internal" href="../../glossary.html#term-warm-start"><span class="xref std std-term">the Glossary</span></a>.</p>
</dd>
<dt><strong>n_jobs</strong><span class="classifier">int or None, optional (default=None)</span></dt><dd><p>The number of jobs to run in parallel for both <a class="reference internal" href="#sklearn.ensemble.BaggingRegressor.fit" title="sklearn.ensemble.BaggingRegressor.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fit</span></code></a> and
<a class="reference internal" href="#sklearn.ensemble.BaggingRegressor.predict" title="sklearn.ensemble.BaggingRegressor.predict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">predict</span></code></a>. <code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a
<a class="reference external" href="https://joblib.readthedocs.io/en/latest/parallel.html#joblib.parallel_backend" title="(in joblib v0.14.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context. <code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all
processors. See <a class="reference internal" href="../../glossary.html#term-n-jobs"><span class="xref std std-term">Glossary</span></a> for more details.</p>
</dd>
<dt><strong>random_state</strong><span class="classifier">int, RandomState instance or None, optional (default=None)</span></dt><dd><p>If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by <code class="docutils literal notranslate"><span class="pre">np.random</span></code>.</p>
</dd>
<dt><strong>verbose</strong><span class="classifier">int, optional (default=0)</span></dt><dd><p>Controls the verbosity when fitting and predicting.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Attributes</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>base_estimator_</strong><span class="classifier">estimator</span></dt><dd><p>The base estimator from which the ensemble is grown.</p>
</dd>
<dt><strong>n_features_</strong><span class="classifier">int</span></dt><dd><p>The number of features when <a class="reference internal" href="#sklearn.ensemble.BaggingRegressor.fit" title="sklearn.ensemble.BaggingRegressor.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fit</span></code></a> is performed.</p>
</dd>
<dt><strong>estimators_</strong><span class="classifier">list of estimators</span></dt><dd><p>The collection of fitted sub-estimators.</p>
</dd>
<dt><a class="reference internal" href="#sklearn.ensemble.BaggingRegressor.estimators_samples_" title="sklearn.ensemble.BaggingRegressor.estimators_samples_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">estimators_samples_</span></code></a><span class="classifier">list of arrays</span></dt><dd><p>The subset of drawn samples for each base estimator.</p>
</dd>
<dt><strong>estimators_features_</strong><span class="classifier">list of arrays</span></dt><dd><p>The subset of drawn features for each base estimator.</p>
</dd>
<dt><strong>oob_score_</strong><span class="classifier">float</span></dt><dd><p>Score of the training dataset obtained using an out-of-bag estimate.
This attribute exists only when <code class="docutils literal notranslate"><span class="pre">oob_score</span></code> is True.</p>
</dd>
<dt><strong>oob_prediction_</strong><span class="classifier">ndarray of shape (n_samples,)</span></dt><dd><p>Prediction computed with out-of-bag estimate on the training
set. If n_estimators is small it might be possible that a data point
was never left out during the bootstrap. In this case,
<code class="docutils literal notranslate"><span class="pre">oob_prediction_</span></code> might contain NaN. This attribute exists only
when <code class="docutils literal notranslate"><span class="pre">oob_score</span></code> is True.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">References</p>
<dl class="citation">
<dt class="label" id="r4d113ba76fc0-1"><span class="brackets"><a class="fn-backref" href="#id1">R4d113ba76fc0-1</a></span></dt>
<dd><p>L. Breiman, “Pasting small votes for classification in large
databases and on-line”, Machine Learning, 36(1), 85-103, 1999.</p>
</dd>
<dt class="label" id="r4d113ba76fc0-2"><span class="brackets"><a class="fn-backref" href="#id2">R4d113ba76fc0-2</a></span></dt>
<dd><p>L. Breiman, “Bagging predictors”, Machine Learning, 24(2), 123-140,
1996.</p>
</dd>
<dt class="label" id="r4d113ba76fc0-3"><span class="brackets"><a class="fn-backref" href="#id3">R4d113ba76fc0-3</a></span></dt>
<dd><p>T. Ho, “The random subspace method for constructing decision
forests”, Pattern Analysis and Machine Intelligence, 20(8), 832-844,
1998.</p>
</dd>
<dt class="label" id="r4d113ba76fc0-4"><span class="brackets"><a class="fn-backref" href="#id4">R4d113ba76fc0-4</a></span></dt>
<dd><p>G. Louppe and P. Geurts, “Ensembles on Random Patches”, Machine
Learning and Knowledge Discovery in Databases, 346-361, 2012.</p>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">SVR</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">BaggingRegressor</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_regression</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_regression</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">n_features</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
<span class="gp">... </span>                       <span class="n">n_informative</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">n_targets</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="gp">... </span>                       <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">regr</span> <span class="o">=</span> <span class="n">BaggingRegressor</span><span class="p">(</span><span class="n">base_estimator</span><span class="o">=</span><span class="n">SVR</span><span class="p">(),</span>
<span class="gp">... </span>                        <span class="n">n_estimators</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">regr</span><span class="o">.</span><span class="n">predict</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]])</span>
<span class="go">array([-2.8720...])</span>
</pre></div>
</div>
<p class="rubric">Methods</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.ensemble.BaggingRegressor.fit" title="sklearn.ensemble.BaggingRegressor.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>(self, X, y[, sample_weight])</p></td>
<td><p>Build a Bagging ensemble of estimators from the training</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.ensemble.BaggingRegressor.get_params" title="sklearn.ensemble.BaggingRegressor.get_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code></a>(self[, deep])</p></td>
<td><p>Get parameters for this estimator.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.ensemble.BaggingRegressor.predict" title="sklearn.ensemble.BaggingRegressor.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(self, X)</p></td>
<td><p>Predict regression target for X.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.ensemble.BaggingRegressor.score" title="sklearn.ensemble.BaggingRegressor.score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">score</span></code></a>(self, X, y[, sample_weight])</p></td>
<td><p>Return the coefficient of determination R^2 of the prediction.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.ensemble.BaggingRegressor.set_params" title="sklearn.ensemble.BaggingRegressor.set_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code></a>(self, \*\*params)</p></td>
<td><p>Set the parameters of this estimator.</p></td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="sklearn.ensemble.BaggingRegressor.__init__">
<code class="sig-name descname">__init__</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">base_estimator=None</em>, <em class="sig-param">n_estimators=10</em>, <em class="sig-param">max_samples=1.0</em>, <em class="sig-param">max_features=1.0</em>, <em class="sig-param">bootstrap=True</em>, <em class="sig-param">bootstrap_features=False</em>, <em class="sig-param">oob_score=False</em>, <em class="sig-param">warm_start=False</em>, <em class="sig-param">n_jobs=None</em>, <em class="sig-param">random_state=None</em>, <em class="sig-param">verbose=0</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/ensemble/_bagging.py#L970"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.ensemble.BaggingRegressor.__init__" title="Permalink to this definition">¶</a></dt>
<dd><p>Initialize self.  See help(type(self)) for accurate signature.</p>
</dd></dl>

<dl class="method">
<dt id="sklearn.ensemble.BaggingRegressor.estimators_samples_">
<em class="property">property </em><code class="sig-name descname">estimators_samples_</code><a class="headerlink" href="#sklearn.ensemble.BaggingRegressor.estimators_samples_" title="Permalink to this definition">¶</a></dt>
<dd><p>The subset of drawn samples for each base estimator.</p>
<p>Returns a dynamically generated list of indices identifying
the samples used for fitting each member of the ensemble, i.e.,
the in-bag samples.</p>
<p>Note: the list is re-created at each call to the property in order
to reduce the object memory footprint by not storing the sampling
data. Thus fetching the property may be slower than expected.</p>
</dd></dl>

<dl class="method">
<dt id="sklearn.ensemble.BaggingRegressor.fit">
<code class="sig-name descname">fit</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y</em>, <em class="sig-param">sample_weight=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/ensemble/_bagging.py#L220"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.ensemble.BaggingRegressor.fit" title="Permalink to this definition">¶</a></dt>
<dd><dl class="simple">
<dt>Build a Bagging ensemble of estimators from the training</dt><dd><p>set (X, y).</p>
</dd>
</dl>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>The training input samples. Sparse matrices are accepted only if
they are supported by the base estimator.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like of shape (n_samples,)</span></dt><dd><p>The target values (class labels in classification, real numbers in
regression).</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Sample weights. If None, then samples are equally weighted.
Note that this is supported only if the base estimator supports
sample weighting.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd></dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.ensemble.BaggingRegressor.get_params">
<code class="sig-name descname">get_params</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">deep=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L173"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.ensemble.BaggingRegressor.get_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Get parameters for this estimator.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>deep</strong><span class="classifier">bool, default=True</span></dt><dd><p>If True, will return the parameters for this estimator and
contained subobjects that are estimators.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>params</strong><span class="classifier">mapping of string to any</span></dt><dd><p>Parameter names mapped to their values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.ensemble.BaggingRegressor.predict">
<code class="sig-name descname">predict</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/ensemble/_bagging.py#L995"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.ensemble.BaggingRegressor.predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Predict regression target for X.</p>
<p>The predicted regression target of an input sample is computed as the
mean predicted regression targets of the estimators in the ensemble.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>The training input samples. Sparse matrices are accepted only if
they are supported by the base estimator.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>y</strong><span class="classifier">ndarray of shape (n_samples,)</span></dt><dd><p>The predicted values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.ensemble.BaggingRegressor.score">
<code class="sig-name descname">score</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y</em>, <em class="sig-param">sample_weight=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L376"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.ensemble.BaggingRegressor.score" title="Permalink to this definition">¶</a></dt>
<dd><p>Return the coefficient of determination R^2 of the prediction.</p>
<p>The coefficient R^2 is defined as (1 - u/v), where u is the residual
sum of squares ((y_true - y_pred) ** 2).sum() and v is the total
sum of squares ((y_true - y_true.mean()) ** 2).sum().
The best possible score is 1.0 and it can be negative (because the
model can be arbitrarily worse). A constant model that always
predicts the expected value of y, disregarding the input features,
would get a R^2 score of 0.0.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>Test samples. For some estimators this may be a
precomputed kernel matrix or a list of generic objects instead,
shape = (n_samples, n_samples_fitted),
where n_samples_fitted is the number of
samples used in the fitting for the estimator.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like of shape (n_samples,) or (n_samples, n_outputs)</span></dt><dd><p>True values for X.</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Sample weights.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>score</strong><span class="classifier">float</span></dt><dd><p>R^2 of self.predict(X) wrt. y.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Notes</p>
<p>The R2 score used when calling <code class="docutils literal notranslate"><span class="pre">score</span></code> on a regressor will use
<code class="docutils literal notranslate"><span class="pre">multioutput='uniform_average'</span></code> from version 0.23 to keep consistent
with <a class="reference internal" href="sklearn.metrics.r2_score.html#sklearn.metrics.r2_score" title="sklearn.metrics.r2_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">r2_score</span></code></a>. This will influence the
<code class="docutils literal notranslate"><span class="pre">score</span></code> method of all the multioutput regressors (except for
<a class="reference internal" href="sklearn.multioutput.MultiOutputRegressor.html#sklearn.multioutput.MultiOutputRegressor" title="sklearn.multioutput.MultiOutputRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">MultiOutputRegressor</span></code></a>). To specify the
default value manually and avoid the warning, please either call
<a class="reference internal" href="sklearn.metrics.r2_score.html#sklearn.metrics.r2_score" title="sklearn.metrics.r2_score"><code class="xref py py-func docutils literal notranslate"><span class="pre">r2_score</span></code></a> directly or make a custom scorer with
<a class="reference internal" href="sklearn.metrics.make_scorer.html#sklearn.metrics.make_scorer" title="sklearn.metrics.make_scorer"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_scorer</span></code></a> (the built-in scorer <code class="docutils literal notranslate"><span class="pre">'r2'</span></code> uses
<code class="docutils literal notranslate"><span class="pre">multioutput='uniform_average'</span></code>).</p>
</dd></dl>

<dl class="method">
<dt id="sklearn.ensemble.BaggingRegressor.set_params">
<code class="sig-name descname">set_params</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">**params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L205"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.ensemble.BaggingRegressor.set_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Set the parameters of this estimator.</p>
<p>The method works on simple estimators as well as on nested objects
(such as pipelines). The latter have parameters of the form
<code class="docutils literal notranslate"><span class="pre">&lt;component&gt;__&lt;parameter&gt;</span></code> so that it’s possible to update each
component of a nested object.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>**params</strong><span class="classifier">dict</span></dt><dd><p>Estimator parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>Estimator instance.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

</dd></dl>

<div class="section" id="examples-using-sklearn-ensemble-baggingregressor">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.ensemble.BaggingRegressor</span></code><a class="headerlink" href="#examples-using-sklearn-ensemble-baggingregressor" title="Permalink to this headline">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="This example illustrates and compares the bias-variance decomposition of the expected mean squa..."><div class="figure align-default" id="id9">
<img alt="../../_images/sphx_glr_plot_bias_variance_thumb.png" src="../../_images/sphx_glr_plot_bias_variance_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/ensemble/plot_bias_variance.html#sphx-glr-auto-examples-ensemble-plot-bias-variance-py"><span class="std std-ref">Single estimator versus bagging: bias-variance decomposition</span></a></span><a class="headerlink" href="#id9" title="Permalink to this image">¶</a></p>
</div>
</div><div class="clearer"></div></div>
</div>


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